Small-world anatomical networks in the human brain revealed by cortical thickness from MRI.

By Yong He, Zhang John Chen, Alan C. Evans. Cerebral Cortex, 2007

1,346 citations recorded by the source corpus.

An important issue in neuroscience is the characterization for the underlying architectures of complex brain networks. However, little is known about the network of anatomical connections in the human brain. Here, we investigated large-scale anatomical connection patterns of the human cerebral cortex using cortical thickness measurements from magnetic resonance images. Two areas were considered anatomically connected if they showed statistically significant correlations in cortical thickness and we constructed the network of such connections using 124 brains from the International Consortium for Brain Mapping database. Significant short- and long-range connections were found in both intra- and interhemispheric regions, many of which were consistent with known neuroanatomical pathways measured by human diffusion imaging. More importantly, we showed that the human brain anatomical network had robust small-world properties with cohesive neighborhoods and short mean distances between regions that were insensitive to the selection of correlation thresholds. Additionally, we also found that this network and the probability of finding a connection between 2 regions for a given anatomical distance had both exponentially truncated power-law distributions. Our results demonstrated the basic organizational principles for the anatomical network in the human brain compatible with previous functional networks studies, which provides important implications of how functional brain states originate from their structural underpinnings. To our knowledge, this study provides the first report of small-world properties and degree distribution of anatomical networks in the human brain using cortical thickness measurements.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7a7971e33fe95a7e1efc776a447b09886f5cb188

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease (2010, background): “Some recent studies demonstrated that small-world properties are exhibited in functional brain networks [23,28] and structural brain networks [24,29].”
Age- and gender-related differences in the cortical anatomical network (2009, methodology): “Functional and morphological networks of human brains have been explored using a variety of structural and functional imaging methods (Achard et al., 2006; Bassett et al., 2006, 2008; He et al., 2007, 2008, 2009b; Stam et al., 2007).”
A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks (2011, background): “This view has been fueled by the systematic finding of small-world topology in a wide range of human brain networks derived from structural (10), functional (11–13), and diffusion tensor MRI (14).”
Cognitive Neuroscience of Aging (2008, background): “Functional brain networks show evidence of “smallworld properties” (Achard et al. 2006; He et al. 2007), meaning that they are characterized by dense local connectivity with relatively few long-range connections, resulting in a short path length between any pair of regions in the network.”
Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex (2012, methodology): “Such integrated metrics mathematically correspond to the areas under the metric curve and have been applied in recent brain network studies (Bassett et al., 2008; Gong et al., 2009a; He et al., 2007, 2008).”
Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “For example, brains share several properties such as small-worldness, over-representation of hub nodes, and modularity , with many other complex networks (Stam, 2004; Achard et al., 2006; Achard and Bullmore, 2007; He et al., 2007; Bassett et al., 2008).”
Brain Anatomical Network and Intelligence (2009, methodology): “The average value of these topological properties of the binary and the weighted networks across all the 79 subjects are listed in Table 3 along with the results of previous studies on functional and anatomical human brain networks at a macro scale level [15,16,20,31,32].”
Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits (2010, methodology): “While this definition is currently widely used [11,15,63,70], it provides an indirect estimate and likely an under-estimate of the true length of white matter tracts and axons in these neural systems, which may take convoluted paths to connect a given pair of nodes.”
Network-based statistic: Identifying differences in brain networks (2010, background): “Alternatively, cross-subject correlation in estimates of cortical thickness or volume, derived from structural MRI, provide an alternative means to measure structural associations between nodes (Bassett et al., 2008; He et al., 2007, 2008, 2009).”
On the use of correlation as a measure of network connectivity (2012, methodology): “, 2004) and anatomical brain networks inwhich connectivity ismeasured using correlation in cortical thickness or volume (Bassett et al., 2008; He et al., 2007).”
Complex network measures of brain connectivity: Uses and interpretations (2010, background): “, 2009) quantify brain networks with a small number of neurobiologically meaningful and easily computable measures (Sporns and Zwi, 2004; Achard et al., 2006; Bassett et al., 2006; He et al., 2007; Hagmann et al., 2008).”
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, background): “In humans, anatomical connectivity in vivo has been successfully assessed via cross-correlation analysis of cortical thickness (grey matter) in structural MRI [4,5], revealing topological differences between healthy controls and patients with Alzheimer’s disease [6] and schizophrenia [7].”